1.1. Technical Field
This invention is a method or system to construct at least one profile representing how the actions of an object of investigation are coordinated, the profile(s) being based on computed measures of longitudinal association or temporal contingency that quantify patterns of interaction in repeated measures or time series data that include two or more variables for one individual.
1.2. Description of Related Art
Scientific knowledge often is represented in the form of mathematical models. Prior art related to this invention will be described in the context of computational methods and systems to create, verify, and refine models that represent objects of investigation.
The statistical method is a primary computational method to inform the process of model building. This invention addresses certain fundamental problems that derive from limitations often encountered when the statistical method is a primary means to inform the process of creating, verifying and refining mathematical models. By addressing these problems, this invention facilitates many scientific investigations and practical arts that may benefit from scientific knowledge.
The Appendix is an outline that helps reveal the logical structure of this application.
Model builders generally identify an object to model and abstract variables that may be relevant to its functioning. Then modelers determine how the variables interact in order to inform the process of model construction. To a large extent, mathematical models are verified by the extent to which they accurately represent interactions of their objects in the real world.
The statistical method is an important tool for constructing many mathematical models. The statistical method includes various measures and procedures for revealing interactions that can be modeled.
A primary problem addressed by this invention derives from the fact that the statistical method is best suited to address objects of investigation that are collective entities. Groups, samples, and populations are collective entities.
Section 1.2 of parent patent application Ser. No. 09/470,956 describes many limitations and problems of the prior art. Many of these problems, limitations, and solutions are illustrated in the context of clinical trials. This invention also addresses these problems and limitations but generally in the broader context of complex systems. Section 1.2.2 of this document emphasizes problems more specifically addressed by this invention. Section 2.4 of this document describes how this and the parent invention address the following problems.
The statistical method and mathematical models often are used to investigate complex systems. For example, mathematical models based on statistical analyses of group data have been used to model the apparent effects of cholesterol and other lipid fractions on mortality and major cardiovascular health events. Such models serve important functions. For example, mathematical models have used laboratory data to predict the long-term health effects of new cholesterol lowering drugs. Nevertheless, statistical analyses have important limitations for investigations of complex systems.
Conventional applications of the statistical method are best suited for analyses of cross-sectional data for collective entities and for predicting events such as death that are not recurrent for individuals. Statistical analyses are not as well suited to measure longitudinal associations or temporal contingencies between and among variables within individualsxe2x80x94interactions that become evident in longitudinal, repeated measures, or time series data.
The statistical method does have some functionality for analyzing repeated measures data, especially for groups. For example, the statistical method often is used to analyze change scores such as pre-post differences in clinical trials. However, this functionality becomes limited as the number of repeated measurements increases. This limitation is due to the fact that the number of differences between any two measurements increases rapidly with the number of repeated measurements. In addition, it is not meaningful, appropriate or useful to conduct statistical tests on all differences that are possible when there are more than a few repeated measurements.
The statistical method also includes techniques such as repeated measures analysis of variance. However, the usefulness of such techniques tends to be limited when the levels of one or more independent variables differ across many repeated measurements for each individual. For example, generally it is not feasible with conventional analyses to substitute blood levels of drug for planned doses before rerunning analyses of the effects of treatment on health.
Conventional data analysis procedures are of limited value for supporting detailed yet comprehensive investigations of complex individual systems whose variables may interact in a nonlinear manner. Here is additional information about five problem areas that are mentioned in the preceding statementxe2x80x94individuality, complexity, nonlinearity, comprehensiveness, and detailxe2x80x94together with a statement about the need to address all these problem areas as a set in particular investigations.
The statistical method is best suited for analyses of cross-sectional data for collective entities such as groups. Many statistical descriptions and inferences are about measures of central tendency for groups. Statistical analyses often are based on assignments of individuals to groups such as treated or not treated, responder or non-responder. The results of such analyses apply most directly to collective entities.
The fundamental limitation of the statistical method that involves individuality will be viewed from two perspectives: (1) the application and (2) the discovery of scientific knowledge. Both perspectives will be illustrated by example.
The statistical method often is applied to describe groups and to use sample data to make inferences about populations. Statistical inferences are used to draw generalized conclusions and make predictions. The extent to which generalized conclusions and predictions about collective entities apply to individuals generally is limited. This can be illustrated in the context of group clinical trials. Individual patients are not apt to experience the same safety and efficacy as the average patient in a clinical trial.
The extent to which generalized conclusions and predictions about populations apply to individuals depends on the extent to which individuals are typical of groups. It also depends on the extent to which samples represent populationsxe2x80x94at least with respect to all considerations relevant to particular investigationsxe2x80x94as well as how members of samples are assigned to treatment groups.
Science is accounting for more and more factors that affect the responses of patients to medical treatments. For example, advances in genetics are identifying many ways in which individuals differ in manners that are relevant to disease and response to treatment. People need better ways to individualize treatment.
The fundamental limitation of the statistical method with respect to individuality also has profound implications for scientific discovery. This will be illustrated in the context of functional genomics and proteomics as it involves health disorders and medical treatments.
Now that genomes are being mapped, some high priority tasks are to identify how the products of gene expression function together and to identify how genetic differences that distinguish individuals, such as single nucleotide polymorhpisms, affect biological functions and responses to treatments. Such tasks currently are hampered by a lack of methods that can be applied to individuals to measure how proteins interact to control biological functions, of how treatments affect protein interactions, and of how treatments interact with proteins and health variables. Measurement of such interactions for individuals, as distinct from groups, is becoming increasingly valuable as it becomes easier to identify how individuals differ genetically.
Group assignments and averages tend to obscure effects of genetic differences on health for individuals and their individual responses to treatments. This makes it difficult to identify genetic differences and form classifications that are predictive of health disorders and differential responses to drugs. This in turn makes it difficult to target drugs to the right patients during drug development and during clinical care.
Complexity derives from the fact that individual systems often have many parts, have different types of action, and function in various and changing environments. Furthermore, certain concepts that often are applied to individuals have various manifestations. For example, health of persons is manifested at different levels of measurement hierarchies such as through laboratory measures, signs and symptoms of disorder, measures of physical and mental functioning, and measures of quality of life.
Complexity in the context of empirical investigations often becomes evident by the fact that many variables are available to describe individuals and their environments. Furthermore, many of these variables interact in various combinations. Investigators and practitioners need better methods and systems to quantify, discover, and describe many interactions simultaneously.
Interactions between and among variables that describe complex systems often are not linear. Two aspects of nonlinearity can be illustrated in the context of multiple linear regression, a commonly used statistical procedure for creating mathematical models. Multiple linear regression models describe the functional relationship between a dependent variable, y, and a set of dependent variables, x1, x2, . . . xn.
Two aspects of linearity, proportionality and additivity, can be illustrated with the equation y=4+5x1+2x2. For this equation, each one-unit increase in x1 yields a 5-unit increase in y regardless of the value of x1. This illustrates proportionality. Furthermore, the effects of x1 and x2 in this equation are additive. However, complex systems often manifest nonlinear interactions. People need improved methods and systems to address nonlinearity.
A productive but conventional experimental research strategy is to isolate independent variables and investigate their effects one by one. Such research often is hypothesis drivenxe2x80x94hypotheses that may be rejected by statistical tests based on group data for collective entities. This isolate-and-test strategy tends toward simplified models that do not reveal how many variables, parts, and manifestations of complex systems interact in coordinated manners.
The failure to measure how complex systems interact in coordinated manners is a problem because coordinated action is a hallmark of how complex systems function in interesting and important ways. We need improved methods and systems to investigate how variables, parts, and manifestations of complex systems function together to regulate and sustain themselves as whole individuals that act as agents and respond. Such methods and systems would be more comprehensive of how many variables, parts, and manifestations of individual complex systems interact.
Biology is beginning to recognize the limits of the isolate-and-test strategy. Dr. Leroy Hood and the Institute for Systems Biology advocate systems biology (http://www.systemsbiology.org/workwhat.html). They explicitly recognize that one cannot learn about biological systems by studying one gene or protein at a time. They recognize the need to study interactions within and across levels of biological information. They recognize that complex systems give rise to emergent or systems properties such as abilities of brains to learn and remember.
Dr. Hood has described this new approach to biology as xe2x80x9cdiscovery science.xe2x80x9d He contributed to the initiation of the Human Genome Projectxe2x80x94a prime example of discovery science. xe2x80x9cDiscovery science enumerates the components of particular objects independent of the questions that characterize the hypothesis-driven science commonly practiced in biologyxe2x80x9d (http://www.systemsbiology.org/workhist.html).
A recent article on the yeast galactose-utilization pathway was considered by the authors to demonstrate xe2x80x9cproof-of-principlexe2x80x9d of the systems approach to biology (T. Ideker, V. Thorsson, J. A. Ranish, R. Christmas, J. Buhler, J. K. King, R. Bumgarner, D. R. Goodlett, R. Aebersold, L Hood, Science, 292, 929-934, 2001). Although the objective of this research was xe2x80x9cto build, test, and refine a model of a cellular pathwayxe2x80x9d using, among other things, information about protein-protein interactions, there appears to be no global or comprehensive attempt of measure the interactions using time series data on protein levels.
Although the need for comprehensive methods and systems for measuring interactions has been illustrated in the context of biology, similar problems plague investigations of many other types of complex system.
The need for detailed investigations becomes evident in at least two different ways. First, it often would be valuable to investigate many different variables in particular investigations. This can be illustrated with the rating scales that often are used in clinical trials for antidepressant drugs. Such composite rating scales often include many items measuring different things such as mood, movement, ideation, and sleep. There is need for more effective methods to investigate the effects of drugs both across all items and for detailed investigations of drug effects on individual items.
Second, there is need for more detailed investigations with respect to each of the individual variables that may be investigated, for example, in clinical trials. For example, it may not be enough to investigate how a particular dose of drug affects depression. There also is need to investigate treatment effects as functions of dose or blood levels of drug, episodes of treatment, as well as delay and persistence of response to treatmentxe2x80x94both for individual patients and for groups of patients.
Various techniques have been developed to address at least some of the problems just described. However, the prior art tends to address the particular problems individually. This piecemeal approach does not recognize that all five types of problem are of one cloth. All five types of problem need to be addressed as a set. Tradeoffs between, for example, detail and comprehensiveness for particular investigations should not be forced by the limitations of methods and systems used to process data.
Important aspects of the dynamic involving different strategies of scientific investigation can be discussed in terms of problems in this set. One example is the dynamic between comprehensive and detailed investigations. Distinctions among the sciences themselves such as chemistry, biology, and psychology can be viewed as attempts to limit the comprehensiveness of investigations. A fundamental and productive research strategy is to focus particular efforts on ever more detailed investigations of ever more delimited sets of phenomena. On the other hand, many people recognize the need to investigate complex wholes. We need better methods and systems to accommodate both strategies simultaneously.
U.S. Pat. No. 6,055,491 involves a method and apparatus for analyzing co-evolving time sequences.
U.S. Pat. Nos. 6,249,755 and 5,528,516 involve an apparatus and method for event correlation and problem reporting.
U.S. Pat. No. 6,173,240 presents multidimensional uncertainty analysis.
U.S. Pat. No. 6,134,510 describes a method for detecting synchronicity between several digital measurement series with the aid of a computer.
U.S. Pat. No. 6,098,024 addresses a system for process data association using LaPlace Everett interpolation.
U.S. Pat. No. 6,051,209 covers a method of evaluating the effects of administering external stimuli or a treatment on the brain using positron emission tomography.
Section 1.2.2.4 cites two web pages. Section 4.2.4 also quotes the first of these two web pages. The two web pages are:
Institute for Systems Biology, What is Systems Biology, URL http://www.systemsbiology.org/workwhat.html, Viewed Apr. 10, 2001; and
Institute for Systems Biology, History of Concepts Leading to the Institute, URL http://www.systemsbiology.org/workhist.html, Viewed Apr. 10, 2001.
Section 1.2.2.4 also cites the following article:
T. Ideker, V. Thorsson, J. A. Ranish, R. Christmas, J. Buhler, J. K. King, R. Bumgamer, D. R. Goodlett, R. Aebersold, L Hood, Science, 292, 929-934, 2001.
Data for the hormone data example in Section 4.9 were described and presented in the following citations:
Padmanabhan, V., McFadden, K., Mauger, D. T., Karsch, F. J., and Midgley, A. R. (1997). Neuroendocrine control of follicle-stimulating hormone (FSH) secretion. 1. Direct evidence for separate episodic and basal components of FSH secretion. Endocrinology 138, 424-432, and;
Midgley, A. R., McFadden, K., Ghazzi, M., Karsch, F. J., Brown, M. R., Mauger, D. T., and Padmanabhan, V. (1997). Nonclassical secretory dynamics of LH revealed by hypothalamo-hypophyseal portal sampling of sheep. Endocrine 6, 133-143.
This invention is a method or system to construct at least one profile representing how the actions of an object of investigation are coordinated, the profile(s) being based on computed measures of longitudinal association or temporal contingency that quantify patterns of interaction in repeated measures or time series data that include two or more variables for one individual. Such profiles are called action coordination profiles (ACPs).
ACPs can provide quantitative descriptions of how individual complex systems may control and regulate themselves, of how two or more individual systems may interact, of how complex systems may be controlled or affected by their environments including treatments, and of how individual systems may control or affect their environments.
In practice, ACPs are limited to selected variables and episodes of action for particular objects of investigation. This is illustrated by the examples in Section 4.9. One example involves certain pituitary and reproductive hormones measured every 5 minutes for up to about 12 hours for individual ewes. Another example involves variables considered to affect the Gross Domestic Product of the United States economy using quarterly data for about 42 years.
The title of parent application Ser. No. 09/470,956 is xe2x80x9cComputational Method and System to Perform Empirical Induction.xe2x80x9d Empirical induction involves procedures to draw generalized conclusions and make predictions from data. More specifically, this invention and its parent involve computational procedures to provide high quality generalized conclusions and predictions as high quality is defined in Section 1.2 of the parent application.
The key innovative concept for this invention and its parent comprises a computational method and system specifically designed to process repeated measures and time series data to measure interactions between and among variables for objects of investigation that are individuals. The parent application describes the Method for the Quantitative Analysis of Longitudinal Associations (MQALA).
MQALA and ACPs include an extensive set of computational tools and analytic options that users can select and apply to address many types of problem encountered in scientific investigations and practical affairs. All these tools and analytic options are based on a core set of computational methods or systems.
This invention and its parent are distinct from and often complementary to the statistical method. As such, this invention facilitates scientific investigations of individuals both as individuals and as members of collective entities. For example, these inventions often can be used to facilitate both the individualization of medical care and the conduct of group clinical trials for treatments used to control or manage chronic disorders.
Since ACPs are a direct extension and distinct improvement on the parent application, much material in the parent application also applies to ACPs. Many terms used in this application are defined in Section 2.9 of the parent application.
The following subsections provide a brief summary of the structure of ACPs as well as how they are constructed, functions of ACPs, and how ACPs address limitations of the statistical method as well as the five specific previously identified problems involved in the prior art.
An ACP can be characterized as a set of computed measure values, the set having two dimensions. One dimension represents independent events and a second dimension represents dependent events. Each column or row for the dimension representing independent events corresponds to one of two or more variables or sets of variables or the results of applying certain features used to define independent events. Each column or row for the dimension representing dependent events corresponds to one of two or more variables or sets of variables or the results of applying certain features used to define dependent events.
Table 1 illustrates the general structure of an ACP with 10 variables and only one column or row for each variable. The same variables are used for both dimensions. Rows represent the variables functioning as independent variables (IVs) to define independent events. Columns represent the variables functioning as dependent variables (DVs) to define dependent events. Cells are formed at intersections of rows and columns.
Table 1 uses the symbols xe2x80x9coxe2x80x9d and xe2x80x9c*xe2x80x9d to represent scores or measure values in general. Each cell of Table 1 that contains an xe2x80x9c*xe2x80x9d indicates that the score or measure value was obtained when the variable labeling a row was functioning to define independent events. Each cell of Table 1 that contains an xe2x80x9coxe2x80x9d indicates that the score or measure value was obtained when the variable labeling a column was functioning to define dependent events. There are no measure values for cells on the concordant diagonal, which are represented with the symbol xe2x80x9c-xe2x80x9d. Additional columns and rows would be used to represent Boolean events defined on two or more variables, to represent transition events, or to represent additional ways of defining independent or dependent events.
Each cell in Table 1 that is identified by an xe2x80x9c*xe2x80x9d or an xe2x80x9coxe2x80x9d represents a particular interaction. Particular interactions also can have dimensions. Dimensions for particular interactions represent analysis parameters such as level of the independent variable, level of the dependent variable, delay, persistence, episode length and episode criteria for the independent variable, and episode length and episode criteria for the dependent variable.
The term xe2x80x9cdimensionxe2x80x9d is being used in two contexts. In the context of ACPs, xe2x80x9cdimensionxe2x80x9d refers to variables functioning to define either independent or dependent events. In the context of particular interactions that are represented by cells, xe2x80x9cdimensionxe2x80x9d refers to analysis parameters that may or may not have multiple levels.
The computed measure values in ACPs generally are either longitudinal association scores or values of measures derived from longitudinal association scores. Section 4.4 identifies examples of measures that can be used to construct ACPs. Typically, the magnitude of each score or measure value in ACPs quantifies either the amount of evidence for a longitudinal association or the strength of that association. The signs of longitudinal association scores indicate positive or negative associations. Positive scores indicate that dependent events are more apt to occur in the presence of independent events than in the absence of independent events. Negative scores indicate that dependent events are less apt to occur in the presence of independent events than in the absence of independent events. Zero scores or measure values indicate no evidence for longitudinal associations or temporal contingencies.
Each of the variables used to define independent and dependent events for ACPs would need to be measured or assessed repeatedly for an individual on two or more occasions. In addition, each variable should have the potential to varyxe2x80x94fluctuate in level or recur over timexe2x80x94for the object of investigation represented by the ACP. Variables could be transformed mathematically before computing scores or measure values in ACPs.
ACPs can be portrayed as tables, figures, graphs, and displays. It is recommended that columns and rows in ACPs for particular types of investigation be presented in standardized orders to facilitate comparisons and analyses of profiles for different individuals or for different episodes of action.
Unless otherwise specified, the same variables and features would be used in the same way to define both independent and dependent events for ACPs. This means that people who construct ACPs generally need not identify variables or events as independent or dependent. Furthermore, this is in accord with how complex systems often function. A given event may function as a dependent event with respect to some other events and the same given event may function as an independent event with respect to still other events.
In some cases, events may function in feedback loops to affect more events of the same type. For example, neurotransmitters can have both pre- and post-synaptic receptors so that release of a transmitter can help propagate a signal and feed back to affect release of additional transmitter.
Features of MQALA can be used alone or together with experimental procedures to help distinguish causal from non-causal associations. Some portions of ACPs could remain blank if, for example, investigators determine that it would not be meaningful to consider variables that were under control in experimental investigations to function as dependent variables.
Typically, various analysis parameters would be used to obtain the measure values in ACPs. Level of independent variable and level of dependent variable are required analysis parameters when the variables are dimensional (when a series of values for a variable has more than two different values) and the user of MQALA decides to examine more than two levels.
Another analysis parameter, delay, would be a primary analysis parameter when ACPs are used to investigate the temporal criterion of causal and other predictive relationships (Section 4.8.9). Delay is defined on variables functioning as independent variables. Users of ACPs could specify one or more particular values of delay or a range of values. One ACP or portion of an ACP would be computed for each particular value of delay. In addition or alternatively, one ACP could summarize scores across a range of values of delay.
Additional analysis parameters for interactions that are described in the parent patent application include episode length and episode criterion for independent and dependent variables as well as persistence defined on variables functioning as IVs. Users can define additional analysis parameters. Typically, the same scoring options would be selected for each IV and for each DV that are used in particular ACPs.
Typically, scores actually shown in ACPs would be summary scores. Information about the location of each summary longitudinal association score or derivative measure in an array identifies the conditions that yielded the summary measure. These conditions are defined in terms of features such as analysis parameter levels and Boolean events. Users would be able to drill down from scores shown in summary ACPs to examine scores as functions of analysis parameter levels and in terms of Boolean events.
ACPs are a way of displaying particular types of quantitative information so that it can be used to discover and describe patterns of longitudinal association or temporal contingency between and among variables and events. Use of ACPs to discover and describe patterns in a systematic, comprehensive, and detailed manner will advance the objectives of scientific investigation, the conduct of practical affairs, and decision-making.
The author has coined various terms to describe ACPs and the methodology upon which they are based. These terms emphasize different ways in which the technology is unique and of value.
MQALA can be viewed as a contribution to xe2x80x9ctemporal contingency analysis.xe2x80x9d The contingencies (associations) involve independent and dependent events defined in multidimensional spaces formed primarily by applying analysis parameters and Boolean operators to transformations of repeated measures data including time series.
Independent and dependent events can be defined in great detail. Analysis parameters account for things such as levels of independent (predictor) and dependent (predicted) variables. Optional analysis parameters account for things such as episodes of events. Here is an example of an independent event defined using such parameters and a temporal resolution of one day. Did or did not a given patient meet the criterion on each of a series of days of taking 100 mg or more of a given drug (Drug 1) on 5 out of 7 consecutive days? Additional optional analysis parameters can be used to define temporal aspects (delays and persistencies) of relations between events.
Boolean operators can be applied to events defined with analysis parameters to define additional events called Boolean events that are based on two or more independent or two or more dependent variables. For example, a Boolean independent event could consist of meeting the criterion defined for Drug 1 in the preceding paragraph AND the criterion of taking Drug 2 at a dose of 50 mg or more on 4 out of 6 consecutive days. The presence of such a Boolean AND independent event may be sufficient, for example, to increase the presence of a particular type of dependent event such as the level of a liver enzyme being above the upper limit of normal.
MQALA analyzes such contingencies between independent and dependent events. Many thousands of different events and types of events can be analyzed simultaneously in particular investigations to identify the levels of analysis parameters and the Boolean events that yield the most evidence for associations or interactions.
The xe2x80x9ctemporalxe2x80x9d in xe2x80x9ctemporal contingency analysisxe2x80x9d indicates that MQALA and any particular ACP quantifies and describes the directions and amount of evidence for contingencies (associations or interactions) between and among events as these contingencies are evident in data that are about the individual and are collected over time from the individual. MQALA also quantifies the strength of associations, contingencies, or interactions. MQALA""s capability to analyze temporal contingencies derives from the fact that it is applied to longitudinal, repeated measures, or time-series data as relatively distinct from cross-sectional data.
ACPs also can be described with coined terms such as xe2x80x9caction coordination fingerprints,xe2x80x9d xe2x80x9cmovement coordination fingerprints,xe2x80x9d xe2x80x9cbehavior coordination fingerprints,xe2x80x9d and xe2x80x9cinteraction fingerprints.xe2x80x9d The term xe2x80x9cfingerprintsxe2x80x9d in such descriptions focuses attention on the fact that ACPs can describe that that is characteristic of individuals that may be unique or different from other individuals. In addition, ACPs can be used to describe that that is characteristic of episodes of coordinated action, movement, or behavior for individuals. For example, episodes of coordinated locomotion of horses have been characterized as walk, cantor, trot, and gallop.
The term xe2x80x9cfingerprintsxe2x80x9d in its conventional use refers to the form or structure of skin on the fingers. In contrast, ACPs fingerprint something that is more abstract and conceptualxe2x80x94namely the way actions interact. Interactions indicate coordination. ACPs can fingerprint how individuals function, control, and sustain themselves as well as interact with each other and their environments.
Section 1.2 of this application describes related art in the context of creating, verifying, and refining mathematical models that represent objects in the world. More specifically, the referenced section presents certain limitations and problems related to using the statistical method for this purpose. This section and its subsections describe how MQALA and ACPs help address these limitations and problems.
Both MQALA, which now includes ACPs, and the statistical method are distinct and often complementary computational methods of empirical induction. Computational methods and systems of empirical induction are used to draw generalized conclusions and make predictions from data.
Although both MQALA and the statistical method are computational methods of empirical induction, they are distinct in other key respects. These distinctions include the type of data (evidence) that the two methods are best suited to analyze, the objectives of analyses, the computational procedures themselves, and the type of entities about which conclusions are drawn and predictions are made.
MQALA analyzes repeated measures or time series data for particular individuals. MQALA requires data for at least two variables or types of events. At least one variable must function as an independent variable and at least one variable must function as a dependent variable. Both independent and dependent variables must vary within individuals in order to obtain nonzero longitudinal association or benefit/harm scores.
In contrast to MQALA, the statistical method is best suited to analyze cross-sectional data for groups of individuals. Inferential statistical procedures (as contrasted to descriptive statistical procedures) also generally require data for independent and dependent variables from groups with two or more individuals per group.
Thus, from what has been said about the type of data best suited for analysis by MQALA and the statistical method, the two methods generally rely on different types of evidence for relationships between and among variables. MQALA relies on longitudinal associations (temporal contingencies) between and among variables within individuals. In contrast, the statistical method is best suited to analyze cross-sectional associationsxe2x80x94differences between and among individuals or groups of individuals.
The statistical method is best suited for analyses involving groups of different individuals at one or only a few times. In contrast, MQALA and ACPs are best suited for analyses involving one individual at many different times.
Objectives of analyses conducted with MQALA are to quantify, discover, analyze and describe longitudinal associations (temporal contingencies) between and among variables within individuals. MQALA provides generalized conclusions about longitudinal associations between and among variables for individuals. Such conclusions are generalized over repeated measurements. MQALA does this with a variety of scores including scores presented in the form of ACPs.
MQALA also supports predictions. These predictions are about how individuals will function or respond in the future. Predictions are based on the assumption that past experience can be used to help predict the future.
MQALA supports predictions in at least two related ways. First, generalized conclusions about how an individual has functioned or responded to date can be used to make predictions about how that individual will respond or function in the future. For example, assume that a benefit/harm score based on many repeated measurements of drug dose and blood pressure for a particular patient over the course of the last year indicates that the drug had a substantial beneficial effect for that patient. This score would support the prediction that the same drug would continue to have the same beneficial effect for the same patient over the course of the next month.
MQALA also supports predictions with a feature called predictive indices. Predictive indices are one way to use information from two or more predictors (IVs) or sets of predictors used to define Boolean events to make predictions about a predicted variable (DV). Predictive indices are computed directly from information used to compute particular longitudinal association scores.
MQALA supports direct predictions. That is, the predictions are for the same individual that the data are about. Furthermore, the predictions are for the same variables analyzed with the same analytic options.
MQALA does not directly support inferences from one individual or group of individuals to another. However, MQALA provides scores and other measures that can be analyzed statistically to make such inferences when the scores or measures are available for two or more individuals. This illustrates the complementarity of MQALA and the statistical method.
MQALA is a powerful new set of computational tools for drawing conclusions and making predictions about individuals by providing quantitative descriptions of experience that has been recorded as repeated measures data.
In contrast to MQALA, the statistical method is best suited to describe characteristics of groups and to quantify, discover, analyze and describe cross-sectional associations between and among variables for groups of individuals. In addition, the statistical method includes procedures for using group descriptions to make statistical inferences from samples of individuals to populations of individuals.
Descriptive statistics are best suited to describe groups of individuals. The application of such group descriptions to individuals is indirect. Similarly, statistical inferences generally are for groups rather than for individuals.
Conventional parallel group clinical trials are conducted primarily for the benefit of groups of patients that may be candidates for treatment in the future. This fact often raises ethical questions concerning the patients who actually participate in conventional group clinical trials.
The computational procedures for MQALA and the statistical method differ in several important respects. Unlike the statistical method, MQALA must convert any dimensional series for independent and dependent variables into sets of dichotomous series. All analysis parameters and Boolean events are defined on such dichotomous series. Dichotomous series for independent variables and dichotomous series for dependent variables a re cross-classified to yield 2xc3x972 tables. This procedure can easily yield thousands of 2xc3x972 tables for any particular individual.
MQALA continues by computing standardized longitudinal association or benefit/harm scores for each of these 2xc3x972 tables. These scores are standardized with respect to all scores that are possible given the marginal frequencies of observed 2xc3x972 tables. Standardization allows the scores to be summarized and compared. In addition, stan dardization makes it reasonable to compute overall benefit/harm scores across many dependent variables for particular individuals. Overall benefit/harm scores can be computed with or without differential weights.
Longitudinal association scores, benefit/harm scores, and overall benefit/harm scoresxe2x80x94one score from each of two or more individualsxe2x80x94can be analyzed statistically. This illustrates the complementarity of MQALA and the statistical method.
A key distinction between MQALA and the statistical method involves the type of entities for which the methods are best suited to draw conclusions and make predictions. MQALA draws conclusions and makes predictions about individuals. For MQALA, individuals include populations investigated as wholes. In contrast, the statistical method is best suited to draw generalized conclusions and support predictions about groups and populations of individuals.
Both MQALA and the statistical method are tools for the conduct of objective scientific investigations. Systematic scientific knowledge generally is considered to involve generalized conclusions rather than particulars.
MQALA can be used to make generalized conclusions about individuals. Such conclusions are generalized over time within individuals where time is represented by repeated measurements.
In contrast to MQALA, the statistical method is best suited to draw generalized conclusions about groups. Descriptive statistics generalize across the individuals that comprise groups. Statistical inferences generally are based on group comparisons and sample data. Such results apply only indirectly to individuals.
The following subsections provide additional information about each of the problem areas described in Section 1.2.2 with a focus on the ACP component of MQALA.
Since an ACP is computed from data about an individual, the ACP applies most directly to the individual that the data are about. Section 2.6 of the parent patent application discusses differences between direct, indirect, and doubly indirect predictions together with some advantages of using direct predictions for individuals.
Differences between direct, indirect, and doubly indirect predictions can be illustrated in the context of conventional parallel group clinical trials. Although such trials provide valuable information about both groups and group members, the application of results from such trials to individuals is doubly indirect. One source of indirectness involves the extent to which samples represent populations. A second source of indirectness involves the extent to which particular individuals are typical of average population members.
The parent application also explains how measures of longitudinal association, such as those used in ACPs, can be reliable and valid measures of longitudinal association for individuals. In brief, applying experimental procedures within individuals can enhance validity. Collecting and analyzing data from many repeated measurements can increase reliability.
ACPs can be computed to describe how the parts, variables, and manifestations of unique individuals interact. For example, the US economy is a relatively unique individual. It generally is not feasible to investigate unique individuals by sampling populations and making inferences from the samples. For such reasons, MQALA is better suited than the statistical method to investigate individuals that are unique.
ACPs also can be computed for individuals that may be differentxe2x80x94not typicalxe2x80x94of average individuals. For example, patients with high blood pressure can differ with respect to concurrent disorders, concomitant treatments, gender, race, age and other factors. Conventional strategies for investigating treatments favor homogenous groups of substantial size. It can be difficult to recruit samples of substantial size when many factors differentiate patients. The number of populations that need to be investigated also increases with the number of factors that differentiate patients. The number of populations that need to be investigated and the number of individuals available in each population clearly limit the strategy of investigating homogeneous groups. MQALA, including ACPs, address such problems by providing unique functionality to help enable scientific investigations of individuals.
Scientific investigations, whether of groups or of individuals, have well known advantages such as providing objective and repeatable results. Some unique advantages of conducting scientific investigations of individuals can be considered from the practical and epistemological perspectives.
From a practical perspective, therapy often needs to be individualized because patients differ from one another in their responses and preferences. MQALA appears to be the missing key for providing individualized or personalized health care that is for people with chronic health concerns and based on objective scientific procedures for drawing generalized conclusions and making predictions from data. Chronic disorders and their treatments often are investigated best with repeated measures and time series data.
From an epistemological perspective, MQALA can be used to help discover how individual differences affect susceptibility to disease and response to therapy. Differences relevant to both include genetic differences.
Another reason why MQALA is an important analytic tool is that it helps enable scientific investigations of how individuals interact with their physical and social environments. The uniqueness, richness, and continuity of such interactions appear to be the essence of individual identity.
MQALA, which now includes ACPs, can help address complexity by simultaneously measuring how many variables interact for objects that are individuals. The variables can be internal or external to the object. The interactions can involve variables both within and across levels in measurement hierarchies. The variables can act in different combinations. Any interactions can be positive or negative.
ACPs are a new way to image complexity as it becomes evident in how individuals function, respond, and act as agents. Images of complexity based on ACPs can help users visualize complexity. Visualizing complexity can help make it understandable.
Images of functional and response complexity should be distinguished from images of structural complexity. Brain scans obtained by Computerized Axial Tomography illustrate structural complexity. In contrast, an ACP of an individual""s brain could show how every region of the brain interacts with every other region of the brain. Such an ACP, which would illustrate functional and response complexity, would be easier to understand if it were obtained under a given set of test conditions. ACPs that image functional complexity of individual brains can be computed from a series of functional brain images (Section 4.2.7.2).
ACP images of functional and response complexity can be very extensive. ACPs can have a virtually unlimited number of rows and columns for each dimension. For example, an image showing functional interconnectivity of brain regions could have one row and column for each of the corresponding pixels in the series of functional magnetic resonance images from which it is computed. Additional levels could be added for Boolean events.
The ways in which MQALA addresses functional complexity can be viewed from other perspectives.
One reason why MQALA, including ACPs, is a significant advance in human history is that although human judgment seems to rely heavily on longitudinal associations and temporal contingencies, prior art computational methods and systems for analyzing longitudinal associations have limited functionality. In contrast, computational methods and systems for analyzing cross-sectional associations are well developed.
The importance of longitudinal associations and temporal contingencies in human judgment can be illustrated in the context of clinicians judging the effects of drugs on patient health. Clinicians often judge how individual patients respond to drug challenge, de-challenge, re-challenge, and other changes of dose. Clinicians often plan continued treatment of individual patients in accord with such judgments. Learning from such judgments can be contrasted with learning from conventional group clinical trials. Sections 2.8.2 and 4.2.2.2 of the parent patent application describe many advantages of using MQALA, a computational method and system that can supplement human judgment, to help individualize patient care.
Longitudinal associations and temporal contingencies also appear to play important roles in the workings of nature. The capacity of brains to learn appears to have evolved in a way that allows animals (including people) to learn from temporal contingencies involving stimuli, responses, and reinforcers. Only recently have humans begun to learn by applying the statistical method. Much of human associative learning also appears to involve temporal contingencies and an extension of learning capabilities from emotional and motor responses to more abstract and conceptual entities. Sections 4.2.5 and 4.2.6 of the parent patent application includes a discussion of how classical conditioning, instrumental conditioning, and paired associate learning can be analyzed with MQALA and of how this knowledge can be used to create machines and artificial systems that learn.
MQALA is an important advance in scientific methodology and for the application of technology to achieve human objectives because ACPs can help users visualize functional and response complexity; facilitate the creation of mathematical models of how complex systems function, respond, and act; as well as the creation of artificial systems that learn.
The computational procedures upon which ACPs are based address nonlinearity in at least two primary ways. First, MQALA converts dimensional series into sets of dichotomous series using integrated scales as described in Section 4.1.2 and illustrated in Tables 6 and 7 of the parent patent application. The values of measures portrayed in ACPs are computed from cross-classifications of independent and dependent events as defined on such dichotomous series as illustrated for longitudinal association scores in Section 4.1.1 of the parent application. As such, the computational procedures do not assume that effects of independent variables or events on dependent variables or events are proportional to independent variable levels.
Second, MQALA can use Boolean independent events and Boolean dependent events to help determine if particular combinations of events are associated more with other events than with the same variables considered individually. This use of Boolean events, described in Section 4.1.11 and illustrated in Table 17 of the parent application, addresses the problem of non-additivity as described in Section 1.2.2.3 of this application.
Section 1.2.1.2.1.2 and its subsections in the parent application describe a number of problems involving comprehensiveness and detail in the context of evaluating treatments for health disorders. Section 2.7.1.2.1.2 and its subsections in the parent application describe how MQALA helps address these problems. Furthermore, Section 4.2.1.1 of the parent application includes information about how MQALA addresses problems involving the emergence of system properties and unique entitiesxe2x80x94problems that are included in Section 1.2.1 also of the parent application. ACPs further address the problems involving comprehensiveness as described is Section 1.2.2.4 of this application because of the capability of ACPs to provide quantitative displays of large numbers of interactions simultaneously. Measurement of the interactions for an individual effectively converts the interactions into a multidimensional object that can be visualized, graphed, and subjected to established quantitative methods such as those of morphometrics.
MQALA, including ACPs, is an advance in data processing that helps make systems biology and discovery science possible. Furthermore, MQALA can be applied to many types of complex system in addition to biological systems. This flexibility of application derives from the fact that MQALA can be applied to data for various types of entity much as the statistical method can be applied to data for various types of entity. However and again, a fundamental distinction between the two methods is that MQALA is specifically designed for application to individual entities while the statistical method is best suited for collective entities.
There are at least two ways that ACPs and MQALA can help address the need for detailed investigations. First, ACPs can address interactions between and among a virtually unlimited number of variables. This can, for example, help users avoid problems that can result when investigators lump many health variables into a single health measure.
Second, each score in an ACP typically would be a score that summarizes a multidimensional array of scores. The dimensions of arrays correspond to analysis parameters such as IV level, DV level, episode length, episode criterion, and persistence. Users of ACPs and MQALA would be able to drill down into these arrays to examine in great detail the interactions shown in ACPs.
MQALA is an integrated set of data processing tools built around core computational measurement methods and providing users with many options about how to proceed. These core computational methods measure longitudinal associations between and among variables and events for individual entities that are complex systems. As such, MQALA addresses all of the five problem areasxe2x80x94individuality, complexity, nonlinearity, comprehensiveness, and detailxe2x80x94as a set.
In practice, all things cannot be investigated at once. Investigators are limited by the number of variables that can be measured simultaneously, by the temporal resolution of data, and by the number of repeated measures that can be included in particular investigations. Computer resources for analyzing data are limited. This is one reason why users of MQALA and ACPs will be limited in the number of analysis parameters, analysis parameter levels, and Boolean events that can be included in particular investigations.
Despite such limitations, MQALA facilitates investigations that are both more comprehensive and detailed than investigations conducted with conventional data processing procedures. As such, MQALA can help make better use of data that can be collected now. In addition, several uses of ACPs are designed to help address the research strategy dynamic involving detailed and comprehensive investigations. As examples, Section 4.8.8 describes how ACPs can be used to measure interactions involving different types of action. Section 4.8.11 describes use of ACPs to investigate nested systems. Section 4.8.12 describes use of ACPs to distinguish episodes of action. Section 4.8.4 describes how ACPs can be used to fingerprint individuals, fingerprints that can be used to develop classifications of individuals into groups that are more homogeneous with respect to how they function and interact with their environments. Such uses provide tools both for partitioning the subject matter of science and for examining how the parts interact to form complex systems.
MQALA is based on a tenet that is central to science: If you want to investigate something scientifically, measure it! MQALA measures the interactions between and among variables and events that help reveal how complex individual systems of many types function, regulate, and sustain themselves as well as how they respond to and act upon their environments.
The above objects and other objects, features, and advantages of the present invention are readily apparent from the following detailed description of the best mode for carrying out the invention when taken in connection with the accompanying drawings.